Data-Driven Modeling to Improve Understanding of Human Behavior, Mobility, and Disease Spread

疾病动态模型是重要的工具，用于预测一段时间内的病例和死亡人数，并支持决策者准备和应对传染病爆发。然而，尽管取得了重大进展，但许多模型仍然缺乏对人类行为和流动性的现实表示，这是疾病传播的关键驱动因素。如果不考虑人类行为的复杂性，模型做出准确预测的能力有限，特别是在较长的时间范围内。该项目研究在疾病传播模型中纳入现实的人类行为和流动性，以1）更好地解释人类应对疾病爆发的不同方式，2）改善传染病传播的预测，3）帮助制定最有效的缓解政策。研究人员使用公开可用的数据，以便可以在美国任何县或州快速部署模型，以预测和减轻未来传染性呼吸道疾病的爆发（例如，COVID-19、季节性流感、麻疹和天花）。这些模型可以提供更及时、更准确的预测，帮助公众、关键机构和政策制定者预测未来，并为循证决策提供支持。该项目将为早期职业研究人员提供专业发展机会，并为博士后研究人员、研究生、本科生和高中生提供培训机会。研究人员将使用数据驱动的方法来解释疾病爆发时行为反应的时空变化。他们假设区域变量，如平均收入，年龄，政治倾向与行为反应的空间模式相关，并将利用非常大的数据集来挖掘这些变量与观察到的行为反应之间的关联规则，包括社交距离，呆在家里的行为，口罩使用和疫苗接受。这些关联规则将被用来开发一种新的建模框架，捕捉人类对疾病的反应的时空变化。将实施拟议的建模框架，以弗吉尼亚州费尔法克斯县为案例研究，模拟COVID-19的传播。该框架还将用于规范性分析，以找到未来传染病爆发时的最佳行动方案。研究人员将模拟和优化旨在减轻疾病传播和最大限度地减少社会经济影响的政策措施。该优化将联合收割机自动优化工具与政策、流行病学、卫生地理学和心理学研究人员的专业知识相结合。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Models of disease dynamics are important tools used to predict the numbers of cases and deaths over time and to support policymakers as they prepare for and respond to infectious disease outbreaks. However, despite significant advances, many models still lack realistic representations of human behavior and mobility, which are key drivers of disease spread. Without accounting for the complexity of human behavior, models are limited in their ability to make accurate predictions, especially over longer time horizons. This project investigates the inclusion of realistic human behavior and mobility in models of disease spread to 1) better explain the different ways that humans respond to disease outbreaks, 2) improve predictions of infectious disease spread, and 3) help to prescribe the most effective mitigation policies. The investigators use publicly available data so that models can be rapidly deployed for any county or state in the U.S. to predict and mitigate future outbreaks of infectious respiratory diseases (e.g., COVID-19, seasonal influenza, measles, and smallpox). These models may provide more timely and accurate predictions to help the general public, key institutions, and policymakers anticipate what is to come and provide support for evidence-based policy making. This project will support professional development opportunities for early-career researchers and training opportunities for a postdoctoral researcher, graduate, undergraduate, and high school students in the Aspiring Scientists Summer Internship program.The researchers will use a data-driven approach to explain the spatio-temporal variations in the behavioral response to a disease outbreak. They hypothesize that regional variables such as average income, age, political leaning are associated with spatial patterns of behavioral response, and will leverage very large data sets to mine association rules between such variables and observed behavioral response, including social distancing, stay-at-home behavior, mask usage, and vaccine acceptance. These association rules will be used to develop a novel modeling framework that captures spatio-temporal variations of human response to disease. The proposed modeling framework will be implemented to simulate the spread of COVID-19 using Fairfax County, VA, as a case study. This framework also will be leveraged for prescriptive analytics to find the best course of action in the event of future infectious disease outbreaks. The researchers will simulate and optimize policy measures aimed at mitigating disease spread and minimizing socio-economic impact. This optimization will combine automatic optimization tools with the expertise of researchers in policy, epidemiology, health geography, and psychology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.